This article develops and empirically implements a stock valuation model. The model makes three assumptions: (i) dividend equals a fixed fraction of net earnings-per-share plus noise; (ii) the economy's pricing kernel is consistent with the Vasicek term structure of interest rates; and (iii) the expected earnings growth rate follows a mean-reverting stochastic process. Our parameterization of the earnings process distinguishes long-run earnings growth from current growth and separately measures the characteristics of the firm's business cycle. The resulting stock valuation formula has three variables as input: net earnings-per-share, expected earnings growth and interest rate. Using a sample of individual stocks, our empirical exercise leads to the following conclusions: (1) the derived valuation formula produces significantly lower pricing errors than existing models both in-and out-of-sample; (2) modeling earnings growth dynamics properly is the most crucial for achieving better performance, while modeling the discounting dynamics properly also makes a significant difference; (3) our model's pricing errors are highly persistent over time and correlated across stocks, suggesting the existence of factors that are important in the market's valuation but missing from our model. In addition to pricing stocks, we can apply the model to back out market expectations about the firm's future from its stock price, allowing us to recover the relevant information embedded in the stock price.